Quick start

[1]:

%load_ext autoreload
%autoreload 2

from py3Dinterpolations.core import GridData
from py3Dinterpolations.modelling.interpolation import Interpolator3D
from py3Dinterpolations.plotting.plotting import plot_3d_model

import pandas as pd

Import data from pandas

The package is written so to be able to import data from pandas. The data must be in a pandas dataframe.

Column names could be either default [“X”, “Y”, “Z”,”V”] or custom. In the latter case, the user must specify the column names in the function call.

[2]:

df = pd.read_csv(
    "../../../tests/fixtures/griddata_default_colnames.csv",
)
df.tail()

[2]:
ID X Y Z V
508 ID00 15.194 0.0 8 25.080662
509 ID00 15.194 0.0 7 19.047857
510 ID00 15.194 0.0 6 19.042223
511 ID00 15.194 0.0 5 14.111599
512 ID00 15.194 0.0 4 11.667224

GridData object

This object is designed to store the data that is being interpolated in an efficient manner. It is a wrapper around a pandas dataframe, with capability to return the equivalent numpy array for interaction with interpolation packages (eg. pyKrige, scipy)

[3]:

gd = GridData(df)
gd.data

[3]:
V
ID X Y Z
ID30 62.163 14.336 19 7.523950
18 7.504403
17 10.089374
16 15.369461
15 11.836174
... ... ... ... ...
ID00 15.194 0.000 8 25.080662
7 19.047857
6 19.042223
5 14.111599
4 11.667224

513 rows × 1 columns

Interpolate

Interpolate by calling the Interpolator3D objects, that wraps up all ncessary functions to interpolate the data.

  • Calulcates required 3d grid for prediction

  • Executes preprocessing

  • Fit the model

  • Executes interpolation

[4]:

interpolation = Interpolator3D(
    gd,
    model_type="statistical",
    model_name = "ordinary_kriging",
    model_params = {
        "variogram_model": "spherical",
        "nlags": 15,
        "weight": True,
        "exact_values": False,
        "verbose": True,
        "enable_plotting": True,
    },
    grid_resolution=1,
    preprocess_kwags={
        "normalize_xyz": True,
        "standardize_v": True,
    }
)

Plotting Enabled

Adjusting data for anisotropy...
Initializing variogram model...
Using 'spherical' Variogram Model
Partial Sill: 0.6919671836967347
Full Sill: 1.180064665388263
Range: 0.9483118335673657
Nugget: 0.48809748169152833
../_images/examples_quickstart_7_1.png 
Calculating statistics on variogram model fit...
Q1 = 0.06289183469394131
Q2 = 0.8427221552135199
cR = 0.5645159804206736


[5]:

interpolation.interpolate()

Executing Ordinary Kriging...

3D Visualization with Plotly

The package natively supports matplotlib and plotly for visualization.

[6]:

import plotly.io as pio
# This ensures Plotly output works in multiple places:
# plotly_mimetype: VS Code notebook UI
# notebook: "Jupyter: Export to HTML" command in VS Code
# See https://plotly.com/python/renderers/#multiple-renderers
pio.renderers.default = "plotly_mimetype+notebook"

[7]:

fig = plot_3d_model(
    interpolation.model,
    plot_points=True,
    scale_points=10,
    surface_count=20,
    opacityscale=[(0,0),(1,1)]

)
fig.show()